86
Reddit DevOps. #devops Thanks @reddit2telegram and @r_channels
How do you manage downstream deployments?
I have several go packages and applications I’m working with. For example one contains business logic and data store operations, others are standalone apps, lambda functions, etc.
Deployments for core packages consist of having to manually update each project that needs to support the new version of the package. I.e. the feature may be complete in the business logic, but apps that depend on that code must get recompiled with the new version. For the actual deployment of apps, I use Bitbucket pipelines to perform tasks like uploading a new image to ECS or updating a lambda function.
I have a feeling we’re outgrowing this because it’s getting tough to remember what to update downstream. In the perfect world everything would be running the current version of the base package, however that isn’t always necessary. And I’m working on getting a dependency graph/chart setup, but if there’s a smarter way to handle something like this, I’d love to hear what you all do in these situations.
https://redd.it/1m2pgo1
@r_devops
Next phase SRE interview, what to expect?
Hey folks,
I recently had a technical interview for an SRE role (focused mainly on networking), and just got invited for the next phase a 30-minute virtual interview with the Director of SRE!
The email didn’t include any specific details about what we’ll be discussing
Any idea what to expect from a director level interview? Is it more behavioral, system design, culture fit, or high-level technical discussion?
Would love to hear your experiences or tips on how i should prepare!
https://redd.it/1m2lzxc
@r_devops
Alternatives for a code quality checks and security checks
Hi all, I'm new here and I'm also a junior DevSecOps. I was wondering what you could recommend for a code quality and security check. I'm working for a small company at the moment and they can't afford much, so I was looking for a free but effective alternative. So I'm looking for a free but effective solution. It would also be a good addition to my dissertation to have found a free or cheaper but effective solution.
https://redd.it/1m2hbss
@r_devops
Seeking Arch Advice: Tines story, ECS-hosted webapp session token mismatches
First off is this is not an appropriate place to ask then my apologies.
I'm not a devops guy, nor a dev at all so I'm outside my comfort zone but really have nowhere else to ask.
I have a Tines story that fronts a webapp that I've deployed to ECS. Works fine on one Task but when it scales up it breaks, because it's not designed to be scaled.
When an HTTP Request is called in Tines, the Credential is authorized and an access_token and a session_token are successfully created. However the HTTP Request itself (after the credential) ends up being load balanced to the second Task.. and that fails on 'invalid session token'.
I have not been able to figure this out on the Tines side, so I am experimenting on the AWS side.. Tried stickiness using both LB tokens and Application tokens. Neither works.
I'm asking for ideas on how to resolve this problem, outside of recompiling the Goland webapp,, that's a non-starter.
Can a redis container be added into the same service in ECS? Maybe an elasticache, API Gateway stack?
https://redd.it/1m2fsru
@r_devops
Octopus Deploy reviews for a large enterprise? Worth it long-term?
Curious if folks in big orgs are still happy with Octopus after 2+ years. Does it hold up with hundreds of apps and multi-region infra? Or does it hit a wall eventually?
https://redd.it/1m2d0bm
@r_devops
Mechanism Design Problem
The CAP theorem can be reinterpreted through the lens of mechanism design theory. Each node in the distributed system is a rational agent with preferences over consistency, availability, and partition tolerance.
The impossibility result emerges from the fact that no mechanism exists that can implement all three properties simultaneously while maintaining incentive compatibility and individual rationality.
### 7.2 Algorithmic Game Theory and Nash Equilibria
In the game-theoretic formulation, the CAP theorem shows that no Nash equilibrium exists where all players (nodes) can simultaneously achieve their desired outcomes for all three properties. The theorem thus represents a fundamental limitation of distributed coordination mechanisms.
## 8. Logical Foundations and Proof Theory
### 8.1 Intuitionistic Logic and Constructive Proofs
The CAP theorem's proof has deep connections to intuitionistic logic and constructive mathematics. The impossibility is not merely classical logical negation but represents a constructive impossibility - there exists no algorithm that can construct a system satisfying all three properties.
In the language of type theory:
```
¬∃(s: System). Consistent(s) ∧ Available(s) ∧ PartitionTolerant(s)
```
This is provable constructively, meaning we can exhibit a specific contradiction for any proposed system.
### 8.2 Modal Logic and Possible Worlds Semantics
Using modal logic, the CAP theorem can be expressed as:
```
□(C ∧ A ∧ P) ≡ ⊥
```
In possible worlds semantics, this means there exists no possible world (network configuration) where all three properties hold simultaneously.
## 9. Information Geometry and Statistical Manifolds
### 9.1 Fisher Information and Consensus Efficiency
The efficiency of distributed consensus protocols can be analyzed using information geometry. The Fisher information matrix encodes the sensitivity of the consensus process to parameter changes, and the CAP constraints impose bounds on the achievable Fisher information.
The Cramér-Rao bound provides a lower bound on the variance of distributed estimators, showing that the CAP trade-offs are reflected in the fundamental limits of statistical inference in distributed systems.
### 9.2 Exponential Families and Maximum Entropy
The space of distributed system configurations can be parameterized as an exponential family with sufficient statistics corresponding to the CAP properties. The maximum entropy principle then provides a natural way to understand the trade-offs between these properties.
## 10. Conclusion: Toward a Unified Field Theory of Distributed Systems
The CAP theorem represents more than a practical constraint on distributed systems; it reveals deep mathematical structures that connect distributed computing to fundamental areas of mathematics and physics. The impossibility is not merely technical but reflects profound limitations rooted in the nature of information, space, and time.
By examining the theorem through multiple mathematical lenses - topology, category theory, quantum mechanics, differential geometry, and logic - we gain insight into the fundamental nature of distributed computation and its relationship to the physical universe. The CAP theorem thus serves as a bridge between computer science and the deeper mathematical structures that govern reality itself.
The implications extend beyond distributed systems to questions of consciousness, knowledge, and the nature of information itself. In a universe where information cannot travel faster than light, the CAP theorem may represent a fundamental constraint on the structure of reality itself.
## References
1. Brewer, Eric. "Towards robust distributed systems"
2. Gilbert, Seth, and Nancy Lynch. "Brewer's conjecture and the feasibility of consistent, available, partition-tolerant web services"
3. Hatcher, Allen. "Algebraic Topology"
4. Nielsen, Michael A., and Isaac L. Chuang. "Quantum Computation and Quantum Information"
5. Mac Lane, Saunders. "Categories for the Working Mathematician"
6. Awodey, Steve. "Category
CAP Theorem: A more nuanced look from an experienced developer and mathematician
The CAP theorem, originally formulated by Eric Brewer and subsequently proven by Gilbert and Lynch, represents a fundamental impossibility result in distributed systems theory. This treatise examines the theorem through the lens of algebraic topology, quantum information theory, and category-theoretic foundations, revealing deep connections to the fundamental limits of information propagation in spacetime and the topological constraints of distributed consensus protocols.
## 1. Theoretical Foundations and Metamathematical Structure
### 1.1 The Brewer Conjecture as Topological Invariant
The CAP theorem can be reinterpreted as a statement about the topological invariants of distributed system configurations. Let us define a distributed system as a simplicial complex K where:
- Vertices represent computational nodes
- Edges represent communication channels
- Higher-dimensional simplices represent coordinated operations
The CAP properties can then be formalized as cohomological invariants:
```
Let H^n(K; R) be the n-th cohomology group of K with coefficients in ring R
Let C: H^0(K; Z₂) → {0,1} be the consistency functional
Let A: H^1(K; Z₂) → {0,1} be the availability functional
Let P: H^2(K; Z₂) → {0,1} be the partition tolerance functional
Then the CAP theorem states: C(K) + A(K) + P(K) ≤ 2 for all connected K
```
### 1.2 Information-Theoretic Formalization via Channel Capacity
From an information-theoretic perspective, the CAP theorem emerges from the fundamental limits of information transmission in noisy channels. Consider a distributed system as a quantum channel Φ: B(H₁) → B(H₂) where B(H) represents the bounded operators on Hilbert space H.
The consistency requirement demands that all observers share identical quantum states |ψ⟩, implying perfect quantum error correction. The availability requirement demands that the channel capacity C(Φ) remains positive under all conditions. The partition tolerance requirement allows for arbitrary noise in the quantum channel.
By the quantum no-cloning theorem and the Holevo bound, we can show that:
```
C(Φ) ≤ max_{ρ} S(Φ(ρ)) - S(Φ(ρ)|ρ)
```
Where S denotes the von Neumann entropy. The impossibility of satisfying all three CAP properties simultaneously follows from the incompatibility of quantum error correction with maintaining channel capacity under arbitrary noise.
## 2. Categorical Semantics of Distributed Consensus
### 2.1 The Category of Distributed States
Define the category **DistSys** where:
- Objects are distributed system states
- Morphisms are state transitions preserving invariants
- Composition represents sequential operations
The CAP properties can be understood as functors from **DistSys** to the category of Boolean algebras:
```
C: DistSys → Bool (Consistency functor)
A: DistSys → Bool (Availability functor)
P: DistSys → Bool (Partition tolerance functor)
```
The CAP theorem then states that there exists no natural transformation that makes the diagram commute for all three functors simultaneously.
### 2.2 Sheaf-Theoretic Interpretation of Global State
The notion of global consistency in distributed systems can be formalized using sheaf theory. Let X be the topological space of system nodes with the network topology, and let F be the sheaf of local states.
Global consistency requires that the global sections Γ(X, F) form a coherent view. However, when network partitions occur, the topology of X becomes disconnected, and the sheaf condition fails:
```
For open cover {Uᵢ} of X:
F(U) ≠ lim←ᵢ F(Uᵢ ∩ Uⱼ)
```
This sheaf-theoretic perspective reveals that consistency is fundamentally about the ability to lift local observations to global knowledge, which becomes impossible under partition conditions.
## 3. Temporal Logic and Distributed Consensus
### 3.1 Linear Temporal Logic Formalization
The CAP properties can be expressed in Linear Temporal Logic (LTL) as:
```
Consistency: □(∀x,y ∈ Nodes. read(x) = read(y))
Availability: □◇(∀x ∈ Nodes.
Automating scaffold changes across multiple python repos
I'm a software engineer that is responsible for maintaining 40-50 repositories on github for my data science team. We are a startup, and there are still a lot of things that we want to change over time. A lot of our repositories are built with python code. Most of the repos are quite similar, it's usually just the underlying python code that changes. Our team works on individual laptops.
I have a scaffold repository that works with cookiecutter, and I want to update over time. The scaffold includes pre-commit/linting configs, dockerfile, python versions, ci/cd definitions. I want to push changes in the scaffold repo across all repositories in github to try to keep things as consistent across repos as possible. I've asked my team to pull stuff as things change, but most repos do not get updated. At the same time, I need people to be able to make modifications across any repository for something that is custom.
I've looked at using git submodules/subtrees for each individual file of the scaffold, and used git-xargs to open prs across multiple repos. There were enough differences between 5 repos that auto-merging the PRs wasn't working, and used https://github.com/dlvhdr/gh-dash to check what PRs were still open due to conflicts (This could be done with a script instead).
Has anyone managed a similar setup? Any alternatives you found for this?
https://redd.it/1m24wny
@r_devops
Trying to break into CS - worth doing a conversion master’s? + CV feedback please!
Hey all, I’m hoping to get some advice. I’ve been working in Tech for about 6 years, mostly on the business/marketing side. More recently, I took on a junior data analyst role, but it’s still quite marketing/business focused rather than purely technical.
This September, I’m planning to start a part-time conversion master’s in Computer Science (my company is sponsoring me) to properly pivot into the CS field.
I’m wondering:
1. Is it actually worth doing a conversion master’s in CS, given my background?
2. See my CV https://imgur.com/a/K3YTaKc, does it look okay for someone trying to break into CS? Anything you’d suggest changing or adding?
Any feedback or thoughts would be massively appreciated! Thanks 😊
https://redd.it/1m24gt0
@r_devops
I built a tiny Windows service wrapper for production use - looking for feedback
Hi all,
Over the past couple of months, I've been having to wrap apps, scripts & utilities as WIndows Services for a few projects at work. Tools like WInSW & NSSM do exist, but I seem to keep running into bugs or missing features - especially around log rotation, management & restarting behaviour.
This led me to build WInLet \-a tiny, production-focused WIndows service wrapper we now use internally at work. It's really built to be simple to use and to offer proper support for log management, env vars, restart policies & so on.
Key features:
Run any script or executable as a Windows Service
A plethora of log management configurations - rotation, compression, etc
Configurable auto-restart on failure
Tiny footprint
Easy-to-read TOML configuration
Example config:
Example config (with full logging and health check):
[service]
name = "my-web-api"
display_name = "My Web API"
description = "Production web API with monitoring"
[process]
executable = "node"
arguments = "server.js"
working_directory = "C:\\Apps\\MyWebAPI"
shutdown_timeout_seconds = 45
[process.environment]
NODE_ENV = "production"
PORT = "3000"
DATABASE_URL = "postgresql://db-server/myapi"
[logging]
level = "Information"
log_path = "C:\\Logs\\MyWebAPI"
mode = "RollBySizeTime"
size_threshold_kb = 25600
time_pattern = "yyyyMMdd"
auto_roll_at_time = "02:00:00"
keep_files = 14
zip_older_than_days = 3
separate_error_log = true
[restart]
policy = "OnFailure"
delay_seconds = 10
max_attempts = 5
window_seconds = 600
[service_account]
username = "DOMAIN\\WebAPIService"
allow_service_logon = true
prompt = "Console"
Install/start it like this:
WinLet.exe install --config my-web-api.toml
WinLet.exe start --name my-web-api
Here's what's coming next - especially as our internal requirements evolve at work:
Prometheus metrics & Windows performance counters
PowerShell module
Hot-reload of config changes
Service dependency graph and bulk operations
Web dashboard for management
I'd love to hear form anyone managing/using Windows services - suggestions, feedback & other use cases you may have are all welcome. Posting in here as well in the hope someone else finds it useful.
Github: ptfpinho23/WinLet: A modern Windows service runner that doesn’t suck.
https://redd.it/1m21ug6
@r_devops
Tell me where the pitfalls are
I've been working with Terraform for a while now. It's been occurring to me that we might be able to use it for user onboarding/offloading. Terraform would have to: make a user in AD, add that person to our github organization, and potentially assign licenses for things like Adobe suite, M365, and some other licenses that dont have a Terraform provider for them, but at this point could be pretty quickly written at least to the extent we need them. And then when someone leaves the company, the licenses would be freed and the users disabled.
But I rarely see people talking about using Terraform this way. I'm curious if anyone has any experience in attempting to use Terraform for user management and what issues you've seen.
https://redd.it/1m1zlul
@r_devops
Best way to prep for CKA?
Hey everyone,
I’m planning to take the **Certified Kubernetes Administrator (CKA)** exam and was wondering:
* What are the best resources/courses you used to prep?
* Any mock labs or hands-on practice you’d recommend?
* Also, any **student discounts** or promo codes available for the exam or courses?
Trying to keep it budget-friendly and efficient. Appreciate any help or advice!
Thanks in advance!
https://redd.it/1m1xj7q
@r_devops
I built an AI tool that turns terminal sessions into runbooks - would love feedback from SREs/DevOps engineers
Hey everyone!
I've been working on Oh Shell! - an AI-powered tool that automatically converts your incident response terminal sessions into comprehensive, searchable runbooks.
**The Problem:**
Every time we have an incident, we lose valuable institutional knowledge. Critical debugging steps, command sequences, and decision-making processes get scattered across terminal histories, chat logs, and individual memories. When similar incidents happen again, we end up repeating the same troubleshooting from scratch.
**The Solution:**
Oh Shell! records your terminal sessions during incident response and uses AI to generate structured runbooks with:
* Step-by-step troubleshooting procedures
* Command explanations and context
* Expected outputs and error handling
* Integration with tools like Notion, Google Docs, Slack, and incident management platforms
Key Features:
* 🎥 One-command recording: Just run ohsh to start recording
* 🤖 AI-powered analysis: Understands your commands and generates comprehensive docs
* 🔗 Tool integrations: Push to Notion, Google Docs, Slack, Firehydrant, [incident.io](http://incident.io)
* 👥 Team collaboration: Share runbooks and build collective knowledge
* 🔒 Security: End-to-end encryption, on-premises options
What I'd love feedback on:
1. Does this solve a real pain point for your team?
1. What integrations would be most valuable to you?
1. How do you currently handle runbook creation and maintenance?
1. What would make this tool indispensable for your incident response process?
1. Any concerns about security or data privacy?
Current Status:
* CLI tool is functional and ready for testing
* Web dashboard for managing generated runbooks
* Integrations with major platforms
* **Free** for trying it out
I'm particularly interested in feedback from SREs, DevOps engineers, and anyone who deals with incident response regularly. What am I missing? What would make this tool better for your workflow?Check it out: [https://ohsh.dev](https://ohsh.dev)
Thanks for your time and feedback!
https://redd.it/1m1uu2v
@r_devops
Discussing about some fatures on a tool for DevOps Engineers that manipulates .env files.
I amimplementing this tool https://github.com/pc-magas/mkdotenv intented to be run insude CI/CD pipelines inoprderto populate `.env` files with secrets.
At future release (0.4.0) the tool would support theese arguments:
MkDotenv VERSION: 0.4.0
Replace or add a variable into a .env file.
Usage:
./bin/mkdotenv-linux-amd64 \
[ --help|-help|--h|-h ] [ --version|-version|--v|-v ] \
--variable-name|-variable-name <variable_name> --variable-value|-variable-value <variable_value> \
[ --env-file|-env-file|--input-file|-input-file <env_file> ] [ --output-file|-output-file <output_file> ] \
[ --remove-doubles|-remove-doubles ] \
Options:
--help, -help, --h, -h OPTIONAL Display help message and exit
--version, -version, --v, -v OPTIONAL Display version and exit
--variable-name, -variable-name REQUIRED Name of the variable to be set
--variable-value, -variable-value REQUIRED Value to assign to the variable
--env-file, -env-file, --input-file, -input-file OPTIONAL Input .env file path (default .env)
--output-file, -output-file OPTIONAL File to write output to (`-` for stdout)
--remove-doubles, -remove-doubles OPTIONAL Remove duplicate variable entries, keeping the first
--remove-doubles be a usable feature my goal is if .env file contains multiple occurences of a variable for example:S3_SECRET="1234"
S3_SECRET="456"
S3_SECRET="999"
--remove-doubles for example in this execution of the command:mkdoptenv --variable-name=S3_SECRET --variable-value="4444" --remove-doubles
S3_SECRET="4444"
mkdoptenv --variable-name=S3_SECRET --variable-value="4444" --remove-doubles --output-file="-" | mkdoptenv --variable-name=S3_KEY --variable-value="XXXX" --remove-doubles
Cloud for SMEs
Hi, I am currently researching the cloud market in Europe.
Want to understand what kind of businesses buy cloud services, why, and through what channels.
Please DM if you can help me with the same - won't take more than 10 mins of your time.
Thanks!!!
https://redd.it/1m1ehhr
@r_devops
Licensing requirements for enterprise deployment
Hello everyone!
BACKGROUND: My organization is a government owned power utility enterprise with a sizeable amount Electrical Engineers (Around 5000). We have a small IT team comprising about 50 engineers. Most of our IT work/application development (Finance/ERP) have been so far managed by contractors.
But of late in house application development has been gaining traction. I have been recently transferred to the IT department to develop an application for the Electrical Power System domain.
My company has strict budget requirements of developing applications with open source technologies only with no cost involvement for software license.
I need to deploy a self hosted centralized version control system with CI CD solution along with a self hosted container registry. I have chosen GitLab Community Edition and Docker Community Edition (Not Docker Desktop, just the Engine and CLI), Docker compose and Harbor as the required technologies.
My Question:
I know all these technologies are open source with MIT and Apache 2.0 licenses. But is there any hidden cost that I may have overlooked particularly for enterprise deployment with such a large scale?
https://redd.it/1m2qaap
@r_devops
Looking for DevOps Intern/Volunteer gigs for real world experience
I'm looking to break into DevOps and am actively seeking part-time roles, internships, or volunteer opportunities to gain practical, hands-on experience.
I have built numerous CI/CD pipelines on Jenkins and GitHub Actions for my side projects, provisioned EKS clusters using Terraform, deployed applications with ArgoCD, and monitored systems with Grafana and Prometheus. I have experience with Docker and Kubernetes and hold the AWS Solutions Architect Associate certification. I recently graduated with my Bachelor of Science in Software Engineering. I also have two years of frontend web development experience as part-time work for startups while I was attending school.
If you have work that needs help with, I would love to join and learn
https://redd.it/1m2l6u5
@r_devops
Browserstation open source alternative of browserbase
We just released BrowserStation, an open source alternative to Browserbase that lets you deploy and manage headless Chrome browsers on your own infra.
It’s built with Kubernetes and Ray, using a sidecar pattern for isolated browser instances and exposes a secure WebSocket proxy for full CDP control.
It integrates with agent frameworks like LangChain and Browser-Use, supports metrics and API key auth, and runs on any cloud or local cluster. Feedback and contributors welcome: https://github.com/operolabs/browserstation
and more info here.
https://redd.it/1m2h3in
@r_devops
AWSCDK appreciation post
Exactly seven years ago today (July 17, 2018), the AWS CDK was publicly announced. I honestly still think it’s one of the most elegant pieces of infrastructure tooling out there. The high-level interface, the design decisions, the focus on developer experience, to me, not many tools today top it (except the CloudFormation part of it).
Over the past year, I’ve been working on bringing that same interface to Terraform. Mainly just to make the same experience available in environments where the original AWS CDK might not have been option just because Terraform has been the standard there.
My hope is for those people who have avoided the AWSCDK because of CFN to give this a try and see if they like it?
Here is the whole cdkworkshop completely ported to terraform: https://aws-workshop.terraconstructs.dev/15-prerequisites.html - let me know what you think?
https://redd.it/1m2cuoe
@r_devops
Theory"
7. Bell, J.S. "On the Einstein Podolsky Rosen paradox"
8. Penrose, Roger. "The Road to Reality"
9. Baez, John C., and Mike Stay. "Physics, Topology, Logic and Computation"
10. Abramsky, Samson, and Bob Coecke. "A categorical semantics of quantum protocols"
https://redd.it/1m29bja
@r_devops
responsive(x))
Partition Tolerance: □◇(∃P ⊆ Nodes. partitioned(P))
```
Where □ denotes "always" and ◇ denotes "eventually".
The CAP theorem can then be proven using the semantic incompatibility of these temporal formulas under the assumption of finite message propagation delays.
### 3.2 Branching Time and Concurrent Histories
In Computation Tree Logic (CTL), the impossibility becomes even more apparent. The branching structure of possible futures creates a tree of computation paths, and the CAP properties constrain which paths are realizable:
```
AG(Consistent ∧ Available ∧ PartitionTolerant) ≡ ⊥
```
This formula is unsatisfiable in any model where network partitions are possible, revealing the fundamental incompatibility at the level of temporal logic semantics.
## 4. Quantum Mechanical Analogies and Bell's Theorem
### 4.1 CAP as Distributed Bell Inequality
The CAP theorem exhibits striking parallels to Bell's theorem in quantum mechanics. Both theorems demonstrate the impossibility of maintaining local realism (consistency) while preserving certain global properties (availability and partition tolerance).
Consider the CAP inequality:
```
⟨C⟩ + ⟨A⟩ + ⟨P⟩ ≤ 2
```
This mirrors the CHSH Bell inequality:
```
|⟨AB⟩ + ⟨AB'⟩ + ⟨A'B⟩ - ⟨A'B'⟩| ≤ 2
```
Both inequalities arise from the fundamental impossibility of reconciling local hidden variables (local state) with global correlations (distributed consensus).
### 4.2 Entanglement and Distributed State Correlation
The desire for strong consistency in distributed systems is analogous to quantum entanglement. Just as entangled particles maintain correlated states regardless of spatial separation, strongly consistent distributed systems maintain correlated state regardless of network topology.
However, the no-communication theorem in quantum mechanics shows that entanglement cannot be used for faster-than-light communication, paralleling how strong consistency cannot be maintained during network partitions without violating availability.
## 5. Differential Geometry and Consensus Manifolds
### 5.1 Configuration Space as Riemannian Manifold
The space of all possible distributed system configurations can be modeled as a Riemannian manifold M with metric tensor g that encodes the "distance" between states in terms of consensus difficulty.
The CAP properties define submanifolds:
- C ⊂ M (consistent configurations)
- A ⊂ M (available configurations)
- P ⊂ M (partition-tolerant configurations)
The CAP theorem states that C ∩ A ∩ P = ∅, meaning these submanifolds do not intersect.
### 5.2 Geodesics and Optimal Consensus Paths
The shortest path between distributed states (geodesics) in this manifold represents optimal consensus protocols. The curvature of the manifold, determined by the Riemann tensor, encodes the fundamental difficulty of achieving consensus.
The CAP theorem emerges from the fact that the manifold has regions of infinite curvature where geodesics cannot exist, corresponding to the impossibility of simultaneous CAP properties.
## 6. Homotopy Theory and Distributed Algorithms
### 6.1 The Fundamental Group of Distributed Computation
The space of distributed computations forms a topological space whose fundamental group π₁(X) captures the essential structure of distributed algorithms. Different consensus protocols correspond to different homotopy classes of paths in this space.
The CAP theorem can be understood as a statement about the non-contractibility of certain loops in this space, meaning that some distributed computations cannot be continuously deformed into trivial (non-distributed) computations.
### 6.2 Obstruction Theory and Impossibility Results
Using obstruction theory, we can show that the CAP constraints create topological obstructions to the existence of certain distributed algorithms. The obstruction classes lie in higher cohomology groups H^n(X; π_{n-1}(Y)), where X is the space of network configurations and Y is the space of desired behaviors.
## 7. Game-Theoretic and Mechanism Design Perspectives
### 7.1 CAP as Multi-Agent
Upcoming changes to the Bitnami catalog
Broadcom introduces Bitnami Secure Images for production-ready containerized applications:
[https://news.broadcom.com/app-dev/broadcom-introduces-bitnami-secure-images-for-production-ready-containerized-applications](https://news.broadcom.com/app-dev/broadcom-introduces-bitnami-secure-images-for-production-ready-containerized-applications)
https://github.com/bitnami/charts/issues/35164
https://redd.it/1m28mag
@r_devops
AI has had no noticeable difference in monitoring / troubleshooting
I obviously use chatgpt to ask for how to debug or some ideas why specific issue might be happening. I also use cursor to create runbooks / alerts / dashboards but that's about it. I have tried a bunch of tools that try to talk to k8s cluster etc but haven't been able to see a noticeable difference generally in debugging. Most of my life is in terminal/logs or dashboards..
One place I have seen though, is in Supabase. They have a cool AI assistant that can query the db / check schema / errors within it's data and do the analysis.
What's the best use-case that you've seen so far that you're repeatedly using? Curious to hear if any of you have been able to validate the AI productivity gain as a DevOps/SRE!
https://redd.it/1m24tjs
@r_devops
Infrastructure automation and self service portal for helpdesk
I hope my question is in the right subreddit. If not, I will remove my post.
I would like to get the community's opinion on a work-related topic. I am a network engineer and I want to implement network and system automation in my company. I have already developed several Ansible roles to automate the management of Cisco switches. I will also develop playbooks/roles for automating the deployment and configuration of Windows and Linux servers.
This automation has two objectives: to harmonize the configuration of new machines being deployed, and to allow the helpdesk to carry out some basic actions without having to connect directly to the machines. For these actions, I have set up Semaphore UI (https://semaphoreui.com/) and created several tasks. Everything works as expected. However, I find that using Semaphore is not suited for the helpdesk. Reading and understanding Ansible logs requires knowledge of Ansible, which the helpdesk does not have.
So my question is: Should the helpdesk be trained to understand and read Ansible logs, or would it be better to develop an independent web application, with a simpler GUI tailored for the helpdesk, containing easy-to-fill fields and a results table, for example? This web application would use the Semaphore UI API to launch jobs and display the results.
https://redd.it/1m22uwq
@r_devops
Really concerned about AI
I’m a senior platform/devops engineer with 7 years of experience.
Should I be concerned about ai taking our jobs? It’s not that I’m worried about the next year or 5 years but after that.
Agentic AI and AI developers are often talked about and the CEO said the whole platform will be run by agents in 5 years.
Can someone put my mind at ease here?
I’m still early on in my career and will need to be working for the next 20 or 30 years.
https://redd.it/1m21qwc
@r_devops
Need guidance on how to start with devops
I am new to devops and wanna know how to start with learning devops any guidance will be helpful thnx
https://redd.it/1m1z43e
@r_devops
Oracle - Race to Certification 2025
Oracle is allowing free certification till 31st October via their Race to Certification program. If you are interested, sign up for it.
https://education.oracle.com/race-to-certification-2025
https://redd.it/1m1vd2e
@r_devops
devops jobs for Jr level
I'm from India, btech cse student and I'm start learning devops, previously I'm in cybersecurity
can anyone give guidence?
and how about devops job market for Jr level or intern
https://redd.it/1m1lbe8
@r_devops
Help me evaluate my options
Hi, I am the sole developer/devops in an application. The application runs through Wine on Linux because it needs to call a C++ DLL that has Windows dependencies. The DLL works by maintaining state. And it has I/O limitations and whatnot so it needs to run one instance of DLL for every user.
The application runs this way.
Frontend->API->Check if docker container running for that user-> If not create it and call the endpoint exposed from the container.
The container runs image has Wine+some more APIs that call the DLL.
The previous devs created a container on demand for each user and they hosted it in house running docker containers on bare metals. (Yes the application is governmental). Now they want to use AWS. I am now evaluating my options between Fargate and EKS.
I evaluated my options as: Fargate and EKS.
Fargate would make my life easier but I am worried the vendor lock in. What if they decide to use a different servers/in-house later down(for whatever reason). I/someone would need to setup everything again.
EKS would be better for less vendor lock in but it's complexity and the fact that I am going to be just the single guy on the project and jumping between writing C++ and maintaining kubernetes is obviously going to be a pain.
I could use some opinions from the experts. Thanks
https://redd.it/1m1hq11
@r_devops
How often are you seeing bugs in production and how do you handle them?
We have unit tests, some integration tests, CI that runs tests on each push, CD to automate deployments, and manual testing, yet we're still seeing a decent amount of bugs in production. At least a couple almost every week.
I'm curious how often are others seeing bugs and how do you handle passing them back to the dev team to fix? Aside from opening a ticket, how are you handling the politics behind passing work onto a team that you're not on and don't run?
https://redd.it/1m1ehue
@r_devops